A well-known eye examination consists of shining a light into the interior of the eye and visually inspecting the uniformity of the reflected light, normally visible as a red color filling the pupil. Any deviation from uniformity, either in one eye or between the pair of eyes, indicates a potential problem in the patient's visual anatomy. Such light refractive screening is thus a useful tool in patient eye care.
Ocular disorders such as strabismus, various forms of refractive errors (myopia, hyperopia, and astigmatism) and opacities of the ocular media are the leading causes of amblyopia, or vision loss. These combine to cause amblyopia in approximately 5% of the population. However, some form of visual problem, not necessarily leading to amblyopia, may be present in over 20% of children.
Amblyopia does not simply decrease visual acuity. In addition to loss of recognition acuity, there is loss of grating acuity, vernier acuity, sensitivity to contrast, distortions of shape, locations in space, motion and worsening of crowding stimulus. Moreover, amblyopia is the leading cause of monocular vision loss in people under the age of 30.
Although monocular vision loss does not impact intellectual capacity, strabismus and other disfiguring disorders can have a tremendous emotional impact. Undetected need for glasses obviously can make school performance more difficult during crucial early grades. Lack of early school success can compound itself and lead to unfulfilled scholastic potential. Further, there are professions in which outstanding visual performance is required. Delaying recognition of various visual disorders may limit children from these future career choices.
Human visual development can be considered as having three stages: (1) the period of development of visual acuity to approximately 3-5 years of age; (2) the period from which deprivation is effective in causing amblyopia, from a few months to 7-8 years; and (3) the period from which recovery from amblyopia can be attained partially or fully (time of amblyopia to at least teenage years).
Because of the foregoing, clinical intervention in amblyopia is most efficacious if it takes place as soon as possible. Over the last 20 years, investigators have shown that many strabismic and amblyopic states result in abnormal visual experience in early life and these can be prevented or reversed with early detection and intervention. Therefore, identification of the defect at the earliest possible moment is crucial. Some studies have suggested that, for the best possible outcome, this must occur within two years of birth.
Despite this, previous reports have shown the primary care physicians are not always utilizing currently available screening techniques. In fact, one large study estimated that pediatricians are screening less than 40% of children age three or younger. This may be because of impracticality, either from a clinical or practical standpoint.
In fact, the National Institute of Health has made it one of its priorities to improve detection of refractive errors, strabismus, and amblyopia in infants and young children. This priority calls specifically for the study of better, and more cost-effective public health methods for testing visual function in preverbal children. Thus, the inventors have perceived a need for automated photorefractive screening.
In order to perform automated photorefractive screening, accurate models of the eye are necessary. The inventors have determined that it would be useful to have an automated way of generating such models and presenting such models for diagnosis, either automatically or by a physician or other care-giver. Accordingly, the present invention is directed to a system and method for locating and modeling eyes in imagery for automated photorefractive screening, and for enabling determination of the presence of anomalies in the patient's visual anatomy.
The problem solved by the invention differs from other eye and face detection problems. Pupil-tracking may be accomplished via a head-mounted apparatus (for instance, see H. Kawai and S. Tamura, "Eye movement analysis system using fundus images", in Pattern Recognition, 19(1), 1986, pp. 77-84), which fixes the location of the eyes relative to an image. Eye tracking may be accomplished via a constrained updating of an eye model (for instance, see X. Xie, R. Sudhakar and H. Zhuang, "On improving eye feature extraction using deformable templates", in Pattern Recognition Letters, 27(6), 1994, pp. 791-799; A. Yuille and P. Hallinan, "Deformable Templates", Chapter 2 in Active Vision, MIT Press, 1992, pp. 21-38). Face recognition (for instance, see International Conference on Automatic Face and Gesture Recognition, proceedings of, edited by M. Bichsel; 1995; Second International Conference on Automatic Face and Gesture Recognition, proceedings of, 1996, in Killington, Vt.) may be accomplished using a variety of image transformations or feature extractions. Additional information may be found in several general image processing references, including K. Castleman, Digital Image Processing, Prentice-Hall, 1996; R. Haralick, L. Shapiro, Computer and Robot Vision, 1, 2, Addison, 1992; and R. Jain, R. Kasturi and B. Schunck, Machine Vision, McGraw-Hill, 1995.